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White Paper

Not Lost in Translation: Annotating Data for Natural Language Processing

Natural Language Processing (NLP) has entered our lives in a multitude of ways – from email auto-suggestion to voice assistants and chatbots. The flow of natural communication has opened up between humans and machines and the applications are boundless. But the challenges of successfully deploying NLP are huge. Humans speak, write, and express their thoughts in an infinite number of ways. Translating human language into a form that computers can understand requires a vast amount of linguistic training data. Accurate and well-structured training data, to enable supervised learning, can be the differentiator in the NLP space.

Read the white paper to:

  • Understand how functions like Named Entity Recognition, Sentiment Analysis, and Salience Analysis help add layers of meaning to text data and prepare it for use in Artificial Intelligence use cases.
  • Benefit from “lessons learned” in the NLP space with the inclusion of three case studies.
  • Learn what’s next in NLP, including insights on conversational AI and multi-turn dialog.
  • Understand how iMerit can help power your NLP algorithms with linguistic data.
White paper natural language processing data
Data labeling NLP

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